





"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    str			):
 lowerCAmelCase    				= {}

 for key, value in state_dict.items():
  if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
   continue

  lowerCAmelCase    				= key.replace('heads.cmd.mim_head.cls.predictions'  ,     'mmm_image_head'			)
  lowerCAmelCase    				= key.replace('heads.cmd.mlm_head.cls.predictions'  ,     'mmm_text_head'			)
  lowerCAmelCase    				= key.replace('heads.cmd.itm_head.cls'  ,     'itm_head'			)
  lowerCAmelCase    				= key.replace('heads.cmd.itm_head.pooler'  ,     'itm_head.pooler'			)
  lowerCAmelCase    				= key.replace('heads.cmd.clip_head.logit_scale'  ,     'flava.logit_scale'			)
  lowerCAmelCase    				= key.replace('heads.fairseq_mlm.cls.predictions'  ,     'mlm_head'			)
  lowerCAmelCase    				= key.replace('heads.imagenet.mim_head.cls.predictions'  ,     'mim_head'			)
  lowerCAmelCase    				= key.replace('mm_text_projection'  ,     'flava.text_to_mm_projection'			)
  lowerCAmelCase    				= key.replace('mm_image_projection'  ,     'flava.image_to_mm_projection'			)
  lowerCAmelCase    				= key.replace('image_encoder.module'  ,     'flava.image_model'			)
  lowerCAmelCase    				= key.replace('text_encoder.module'  ,     'flava.text_model'			)
  lowerCAmelCase    				= key.replace('mm_encoder.module.encoder.cls_token'  ,     'flava.multimodal_model.cls_token'			)
  lowerCAmelCase    				= key.replace('mm_encoder.module'  ,     'flava.multimodal_model'			)
  lowerCAmelCase    				= key.replace('text_projection'  ,     'flava.text_projection'			)
  lowerCAmelCase    				= key.replace('image_projection'  ,     'flava.image_projection'			)

  lowerCAmelCase    				= value.float()

 for key, value in codebook_state_dict.items():
  lowerCAmelCase    				= value

 return upgrade

@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    Union[str, Any]  ,     _UpperCAmelCase					:    List[Any]=None			):




 if config_path is not None:
  lowerCAmelCase    				= FlavaConfig.from_pretrained(_lowerCamelCase			)
 else:
  lowerCAmelCase    				= FlavaConfig()

 lowerCAmelCase    				= FlavaForPreTraining(_lowerCamelCase			).eval()

 lowerCAmelCase    				= convert_dalle_checkpoint(_lowerCamelCase  ,     _lowerCamelCase  ,     save_checkpoint=_lowerCamelCase			)

 if os.path.exists(_lowerCamelCase			):
  lowerCAmelCase    				= torch.load(_lowerCamelCase  ,     map_location='cpu'			)
 else:
  lowerCAmelCase    				= torch.hub.load_state_dict_from_url(_lowerCamelCase  ,     map_location='cpu'			)

 lowerCAmelCase    				= upgrade_state_dict(_lowerCamelCase  ,     _lowerCamelCase			)
 hf_model.load_state_dict(_lowerCamelCase			)
 lowerCAmelCase    				= hf_model.state_dict()
 lowerCAmelCase    				= count_parameters(_lowerCamelCase			)
 lowerCAmelCase    				= count_parameters(_lowerCamelCase			) + count_parameters(_lowerCamelCase			)

 assert torch.allclose(_lowerCamelCase  ,     _lowerCamelCase  ,     atol=1e-3			)

 hf_model.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
       __UpperCamelCase     :						List[str]						       =  argparse.ArgumentParser()
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
       parser.add_argument('''--codebook_path''', default=None, type=str, help='''Path to flava codebook checkpoint''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       __UpperCamelCase     :						List[str]						       =  parser.parse_args()

       convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''BlipImageProcessor'''
			lowercase        =     ('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  False
				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.image_processor






			def __call__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	ImageInput = None      ,SCREAMING_SNAKE_CASE_  :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, PaddingStrategy] = False      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, TruncationStrategy] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	int = 0      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[bool] = None      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, TensorType]] = None      ,**SCREAMING_SNAKE_CASE_  :	str      ,)       ->	BatchEncoding:





				"""simple docstring"""


				if images is None and text is None:
					raise ValueError('''You have to specify either images or text.'''			)

				# Get only text
				if images is None:
					lowerCAmelCase												=  self.tokenizer
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
					return text_encoding

				# add pixel_values
				lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
				else:
					lowerCAmelCase												=  None

				if text_encoding is not None:
					encoding_image_processor.update(SCREAMING_SNAKE_CASE_			)

				return encoding_image_processor






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


